code: def _collect_data(self) -> Dict[str, Dict[str, torch.Tensor]]:
      data = defaultdict(dict)
      for module_name, hooks in self.hooks.items():
          for target_name, hook in hooks.items():
              if len(hook.buffer) > 0:
                  data[module_name][target_name] = (
                      hook.buffer[0] / self.training_steps
                  )
      return data
  
imports: from __future__ import annotations
  from collections import defaultdict
  import functools
  import logging
  from typing import Callable, Dict, List, Literal, Tuple, overload
  import torch
  from .tools import _METRICS, _MASKS, norm_metrics, generate_sparsity, is_active_target
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import TargetType
  from ..base.wrapper import ModuleWrapper
  from ..utils.docstring import _EVALUATOR_DOCSTRING
  from ..utils.evaluator import Evaluator, TensorHook
label_desc: Use image node_id 5 for calling the TaylorPruner obj's underlying member method code with example usage: obj._collect_data() and returns: data